Optimizing key-value stores for hybrid storage architectures
Bibliographic record
Abstract
Flash-based solid state drives (SSDs) are increas-ingly becoming a popular choice as a storage de-vice within database management systems and key-value stores alike. SSDs offer fast throughput and low latency access to data, but their price-per-byte cost often makes them uneconomical for exclusive use, especially in the era of big data workloads. A common solution to this problem is to augment existing database systems by adding smaller SSDs that target only performance-critical areas. We be-lieve this hybrid approach to be a stop-gap solution. Rather than simply extending existing systems with SSDs, in this work we completely re-architect how a key-value database operates in a hybrid stor-age setting with both small but fast SSDs and slower but high-capacity HDDs. We formulate an accurate I/O cost model to study how popular key-value stores behave under several varying represen-tative workloads. Based on these studies and tak-ing a holistic approach, we design a system that dynamically optimizes the data layout and access strategy that leverages the strengths of each avail-able storage medium. 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".